COmpetition between Verbal and Implicit Systems model of category learning (Ashby et al. 1998), as described in Ashby et al. (2011). The current implementation supports two-category experiments, and uses only single-dimension, not-below-chance, rules in the Explicit system.
slpCOVIS(st, tr, crx = TRUE, respt = FALSE, rgive = TRUE, xtdo = FALSE)List of model parameters
R-by-C matrix of training items
Boolean. Explicit System. If set to TRUE, the current rule is included in the random selection of a rule to receive a weight increase from the Possion distribution. If set to FALSE, the current rule is not included in this random selection.
Set to FALSE for the behaviour described in Note 5; behaviour when TRUE is undocumented
Set to TRUE; FALSE is undocumented
Set to FALSE; TRUE is undocumented
Returns a List containing eight components:
A two-column matrix, representing the model's response on each trial. For any given trial, [1,0] indicates a Category 1 response; [0,1] indicates a Category 2 response. Responses are reported in this manner to facilitate cross-compatibility with models that produce response probabilities on each trial.
Explicit system - rule saliences after final trial
Procedural system - cortico-striatal synaptic strengths after final trial)
Decision system - trust in explicit system after final trial
Decision system - trust in procedural system after final trial
Explicit system - rule used by explicit system on final trial
Implicit system - predicted reward value on final trial
Implicit system - obtained reward value on final trial
The coverage in this help file is relatively brief; for a more extensive tutorial, see Inkster et al. (n.d.).
The function works as a stateful list processor (slp; see Wills et al., 2017). Specifically, it takes a matrix (tr) as an argument, where each row is one trial for the network, and the columns specify the input representation. It returns a List containing the predictions made by the model and the final state of the model, hence its description as a 'stateful' list processor.
Argument st must be a list containing the following
information. Parameter names given in brackets in the descriptions
below follow the naming conventions of Ashby et al. (2011), and
Edmunds & Wills (2016). Equation numbers are from Ashby et al. (2011);
where there is no equation, the page number is given instead.
Explicit system variables:
envar - (sigma^2_E) - p. 68 - Variance of the noise distribution
used to determine which response the explicit system makes on the
current trial. See Note 4, below.
decbound - (C) - Eq. 1 - location of the decision bound on a
single dimension. In the current implementation of slpCOVIS, this
location is the same for all dimensions.
corcon - (delta_c) - Eq. 2 - constant by which to increase
current rule saliency in the case of a correct response.
errcon - (delta_e) - Eq. 3 - constant by which to decrease
current rule saliency in the case of an incorrect response.
perscon - (gamma) - Eq. 4 - perseveration constant, i.e. value
to add to the salience of the current rule to obtain its rule weight.
lambda - (lambda) - Eq. 5 - Mean of the Poission
distribution. A value randomly sampled from the Poisson distribution
is added to a randomly-selected rule when calculating the weights for
new rule selection.
decsto - (a) - Eq. 7 - decision stochasticity when using rule
weights to select the rule for the next trial. For Ashby et
al. (2011)'s implementation, a = 1. For other uses, see Edmunds &
Wills (2016).
Procedural system variables:
sconst - (alpha) - Eq. 8 - scaling constant for cortical unit
activation. See Note 3, below.
invar - (sigma^2_p) - Eq. 9 - Variance of the
normally-distributed noise used to calculate striatal unit activation.
dbase - (D_base) - Eq. 10 - baseline dopamine level.
alphaw - (alpha_w) - Eq. 10 - Learning rate parameter in force
when striatal activation is above the NMDA threshold, and dopamine is
above baseline.
betaw - (beta_w) - Eq. 10 - Learning rate parameter in force
when striatal activation is above the NMDA threshold, and dopamine is
below baseline.
gammaw - (gamma_w) - Eq. 10 - Learning rate parameter in force
when striatal activation is between the AMPA and NMDA thresholds.
nmda - (theta_NMDA) - Eq. 10 - Activation threshold for
post-synaptic NMDA.
ampa - (theta_AMPA) - Eq. 10 - Activation threshold for
post-synaptic AMPA. See Note 1, below.
wmax - (w_max) - Eq. 10 - Intended upper weight limit for a
cortico-striatal link. See Note 2, below.
prep - ( P_(n-1) ) - Eq. 12 - predicted reward value
immediately prior to first trial. If unsure, set to zero.
prer - ( R_(n-1) ) - Eq. 12 - obtained reward value immediately
prior to first trial. If unsure, set to zero.
Competition / decision system variables:
emaxval - p.77 - The maximum possible value of the the Explicit
system's discriminant variable. For example, if the stimulus value
varies from zero to one, and C (see above) is 0.5, then the maximum
value is 1-0.5 = 0.5
etrust - (theta_E) - Eq. 15 - trust in the explicit system
immediately prior to first trial. If unsure, set to .99.
itrust - (theta_P) - p. 77 - trust in the procedural system
immediately prior to first trial. If unsure, set to .01. See also Note
7, below.
ocp - (delta_OC) - Eq. 15 - constant used to increase trust in
the Explicit system after it suggests a response that turns out to be
correct.
oep - (delta_OE) - Eq. 16 - constant used to decrease trust in
the Explicit system after it suggests a response that turns out to be
incorrect.
Initial state of model:
initrules - vector of length stimdim, representing the
initial salience of each single-dimensional rule in the Explicit
system.
crule - a number indicating which rule is in use immediately
prior to the first trial (1 = dimension 1, 2 = dimension 2, etc). If
this is not meaningful in the context of your simulation, set it to
zero, and ensure ctrl = 1 in the first row of your training matrix
(see below). This will then randomly pick an initial rule.
initsy - matrix of stimdim rows and two columns -
contains the initial values for the cortico-striatal connection
strengths.
scups - matrix of stimdim columns and as many rows as
you wish to have cortical input units. Each row represents the
position of a cortical unit in N-dimensional stimulus space.
And finally, a couple of things slpCOVIS needs to interpret your tr matrix (see below):
stimdim - number of stimulus dimensions in the input
representation.
colskip - skip the first N columns of the tr array, where N =
colskip. colskip should be set to the number of optional columns you
have added to matrix tr, PLUS ONE. So, if you have added no optional
columns, colskip = 1. This is because the first (non-optional) column
contains the control values, see below.
Argument tr must be a matrix, where each row is one trial
presented to the network. Trials are always presented to the model in
the order specified. The columns must be as described below, in the
order described below:
ctrl - vector of control codes. Available codes are: 0 = normal
trial, 1 = reset network (i.e. set back to the state defined in list
st and randomly select an initial rule for the Explicit System
using Eq. 7) , 2 = Freeze learning. Control codes are actioned before the
trial is processed.
opt1, opt2, … - optional columns, which may have any names
you wish, and you may have as many as you like, but they must be
placed after the ctrl column, and before the remaining columns (see
below). These optional columns are ignored by this function, but you
may wish to use them for readability. For example, you might include
columns for block number, trial number, and stimulus ID number. The
argument colskip (see above) must be set to the number of optional
columns plus 1.
x1, x2, … - stimulus input to the model; there must be one
column for each stimulus dimension.
t1 - teaching signal to model. If the correct response is
Category 1, t = 1. If the correct response is Category 2, t =
-1. Experiments with something other than two categories are not
supported in the current implementation.
optend1, optend2, … - optional columns, which may have any
names you wish, and you may have as many as you like, but they must be
placed after the t1 column. These optional columns are ignored by this
function, but may help with cross-compatibility with other model
implementations. For example, the additional 't' and 'm' columns of
input representations generated for slpALCOVE will be safely ignored
by slpCOVIS.
Ashby, F.G., Alfonso-Reese, L.A., Turken, A.U. & Waldron, E.M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442-481.
Ashby, F.G., Paul, E.J., & Maddox, W.T. (2011). COVIS. In Pothos, E.M. & Wills, A.J. (2011). Formal approaches in categorization. Cambridge, UK: Cambridge University Press.
Edmunds, C.E.R., & Wills, A.J. (2016). Modeling category learning using a dual-system approach: A simulation of Shepard, Hovland and Jenkins (1961) by COVIS. In A. Papfragou, D. Grodner, D. Mirman, & J.C. Trueswell (Eds.). Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 69-74). Austin, TX: Cognitive Science Society.
Inkster, A.B., Edmunds, C.E.R., & Wills, A.J. (n.d.). A distributed-collaboration resource for dual-process modeling in category learning. Manuscript in preparation.
Pothos, E.M. & Wills, A.J.(2011). Formal approaches in Categorisation.Cambridge: University Press.
Wills, A.J., O'Connell, G., Edmunds, C.E.R., & Inkster, A.B.(2017). Progress in modeling through distributed collaboration: Concepts, tools, and category-learning examples. Psychology of Learning and Motivation, 66, 79-115.